论文标题

建立具有当地差异隐私的协作手机黑名单系统

Building a Collaborative Phone Blacklisting System with Local Differential Privacy

论文作者

Ucci, Daniele, Perdisci, Roberto, Lee, Jaewoo, Ahamad, Mustaque

论文摘要

垃圾邮件电话已经从滋扰到越来越有效的骗局交付工具迅速增长。为了应对这一越来越成功的攻击向量,许多商业智能手机应用程序承诺会在应用商店中出现垃圾邮件电话,现在已有数十万甚至数百万用户使用。但是,按照类似于某些在线社交网络服务类似的业务模型,这些应用程序通常会从几乎没有正式隐私保证的用户手机中收集呼叫记录或其他潜在敏感信息。 在本文中,我们研究是否可以构建实用的协作手机黑名单系统,该系统利用当地差异隐私(LDP)机制来提供明确的隐私保证。我们分析了与使用LDP相关的挑战和权衡,评估我们基于LDP的系统对FTC收集的现实用户报告的呼叫记录,并表明可以使用合理的整体隐私预算学习电话黑名单,并在同一时间保留用户的隐私,同时维持博学的黑名单。

Spam phone calls have been rapidly growing from nuisance to an increasingly effective scam delivery tool. To counter this increasingly successful attack vector, a number of commercial smartphone apps that promise to block spam phone calls have appeared on app stores, and are now used by hundreds of thousands or even millions of users. However, following a business model similar to some online social network services, these apps often collect call records or other potentially sensitive information from users' phones with little or no formal privacy guarantees. In this paper, we study whether it is possible to build a practical collaborative phone blacklisting system that makes use of local differential privacy (LDP) mechanisms to provide clear privacy guarantees. We analyze the challenges and trade-offs related to using LDP, evaluate our LDP-based system on real-world user-reported call records collected by the FTC, and show that it is possible to learn a phone blacklist using a reasonable overall privacy budget and at the same time preserve users' privacy while maintaining utility for the learned blacklist.

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